Large-Scale Image Clustering Based on Camera Fingerprints : A Survey

Authors

  • Kriti Sharma  Department of Computer Science and Engineering, College of Technology and Engineering, Udaipur, Rajasthan, India
  • Dr. Naveen Choudhary  Department of Computer Science and Engineering, College of Technology and Engineering, Udaipur, Rajasthan, India
  • Kalpana Jain  Department of Computer Science and Engineering, College of Technology and Engineering, Udaipur, Rajasthan, India

Keywords:

Large-Scale Data Mining, Image Clustering, Graph Partitioning, Sensor Pattern Noise, Divide-And-Conquer, Digital Forensics

Abstract

In the last decade, sensor pattern noise serves as a finger print to digital camera. Researching about fingerprint is one of key technique in forensic which helps investigator to identify suspect and setup case against them. Similarly identifying "camera fingerprint" could be serve as evidence in court. In this paper we have discussed what are different methods of clustering this large image data set when the number of classes (i.e., the number of cameras) is much higher than the average size of class (i.e., the number of images acquired by each camera). We refer to practical scenarios.

References

  1. J. Lukas, J. Fridrich, and M. Goljan, “Digital camera identification from sensor pattern noise,” IEEE Trans. Inf. Forensics Security, vol. 1, no. 2, pp. 205-214, Jun. 2006.
  2. M. Chen, J. Fridrich, M. Goljan, and J. Lukás, “Determining image origin and integrity using sensor noise,” IEEE Trans. Inf. Forensics Security, vol. 3, no. 1, pp. 74-90, Mar. 2008.
  3. C.-T. Li, “Source camera identification using enhanced sensor pattern noise,” IEEE Trans. Inf. Forensics Security, vol. 5, no. 2, pp. 280-287, Jun. 2010.
  4. X. Kang, Y. Li, Z. Qu, and J. Huang, “Enhancing source camera identification performance with a camera reference phase sensor pattern noise,” IEEE Trans. Inf. Forensics Security, vol. 7, no. 2, pp. 393-402, Apr. 2012.
  5. X. Kang, J. Chen, K. Lin, and P. Anjie, “A con-text-adaptive SPN predictor for trustworthy source camera identification,” EURASIP J. Image Video Process., vol. 2014, no. 1, pp. 1-11, Dec. 2014.
  6. A. Lawgaly, F. Khelifi, and A. Bouridane, “Weighted averaging-based sensor pattern noise estimation for source camera identification,” in Proc. IEEE Int. Conf. Image Process. (ICIP), Oct. 2014, pp. 5357-5361.
  7. X. Lin and C. T. Li, “Enhancing sensor pattern noise via filtering distortion removal,” IEEE Signal Process. Lett., vol. 23, no. 3, pp. 381-385, Mar. 2016.
  8. C.-T. Li, “Unsupervised classification of digital images using enhanced sensor pattern noise,” in Proc. IEEE Int. Symp. Circuits Syst., Jun. 2010, pp. 3429-3432.
  9. B.-B. Liu, H.-K. Lee, Y. Hu, and C.-H. Choi, “On classification of source cameras: A graph based approach,” in Proc. IEEE Int. Workshop Inf. Forensics Secur. (WIFS), Dec. 2010, pp. 1-5.
  10. S. X. Yu and J. Shi, “Multiclass spectral clustering,” in Proc. IEEE Int. Conf. Comput. Vis., Oct. 2003, pp. 313 319.
  11. R. Caldelli, I. Amerini, F. Picchioni, and M. Innocenti, “Fast image clustering of unknown source images,” in Proc. IEEE Int. Workshop Inf. Forensics Secur., Dec. 2010, pp. 1-5.
  12. L. J. G. Villalba, A. L. S. Orozco, and J. R. Corripio, “Smartphone image clustering,” Expert Syst. Appl., vol. 42, no. 4, pp. 1927-1940, 2015.
  13. A. K. Jain and R. C. Dubes, Algorithms For Clustering Data. Englewood Cliffs, NJ, USA: Prentice-Hall, 1988.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2017-06-30

Issue

Volume 2, Issue 3, May-June-2017

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Kriti Sharma, Dr. Naveen Choudhary, Kalpana Jain, " Large-Scale Image Clustering Based on Camera Fingerprints : A Survey , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 3, pp. 433-437, May-June-2017.